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Conclusion

We present a new compressed sensing framework for multishell HARDI. We propose a regularized dictionary learning method for multishell signals, to handle low SNR at high b values and show its advantages in getting improved fits to human brain HARDI. To control overfltting of the dictionary to tracts with unknown orientations, we propose a strong non-sparsity penalty similar to the L0 pseudo-norm. Our framework reconstructs images directly from HARDI data undersampled in gradient directions, thereby allowing an acquisition speedup of the same factor as the gradient-direction undersampling factor. We propose efficient optimization schemes and show results that improve the state of the art.

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